/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    CrossValidationResultProducer.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.experiment;

import weka.core.AdditionalMeasureProducer;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

import java.io.File;
import java.util.Calendar;
import java.util.Enumeration;
import java.util.Random;
import java.util.TimeZone;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> Generates for each run, carries out an n-fold
 * cross-validation, using the set SplitEvaluator to generate some results. If
 * the class attribute is nominal, the dataset is stratified. Results for each
 * fold are generated, so you may wish to use this in addition with an
 * AveragingResultProducer to obtain averages for each run.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -X &lt;number of folds&gt;
 *  The number of folds to use for the cross-validation.
 *  (default 10)
 * </pre>
 * 
 * <pre>
 * -D
 * Save raw split evaluator output.
 * </pre>
 * 
 * <pre>
 * -O &lt;file/directory name/path&gt;
 *  The filename where raw output will be stored.
 *  If a directory name is specified then then individual
 *  outputs will be gzipped, otherwise all output will be
 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of a SplitEvaluator.
 *  eg: weka.experiment.ClassifierSplitEvaluator
 * </pre>
 * 
 * <pre>
 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of the classifier.
 *  eg: weka.classifiers.bayes.NaiveBayes
 * </pre>
 * 
 * <pre>
 * -C &lt;index&gt;
 *  The index of the class for which IR statistics
 *  are to be output. (default 1)
 * </pre>
 * 
 * <pre>
 * -I &lt;index&gt;
 *  The index of an attribute to output in the
 *  results. This attribute should identify an
 *  instance in order to know which instances are
 *  in the test set of a cross validation. if 0
 *  no output (default 0).
 * </pre>
 * 
 * <pre>
 * -P
 *  Add target and prediction columns to the result
 *  for each fold.
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * All options after -- will be passed to the split evaluator.
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision: 1.17 $
 */
public class CrossValidationResultProducer implements ResultProducer,
		OptionHandler, AdditionalMeasureProducer, RevisionHandler {

	/** for serialization */
	static final long serialVersionUID = -1580053925080091917L;

	/** The dataset of interest */
	protected Instances m_Instances;

	/** The ResultListener to send results to */
	protected ResultListener m_ResultListener = new CSVResultListener();

	/** The number of folds in the cross-validation */
	protected int m_NumFolds = 10;

	/** Save raw output of split evaluators --- for debugging purposes */
	protected boolean m_debugOutput = false;

	/** The output zipper to use for saving raw splitEvaluator output */
	protected OutputZipper m_ZipDest = null;

	/** The destination output file/directory for raw output */
	protected File m_OutputFile = new File(new File(
			System.getProperty("user.dir")), "splitEvalutorOut.zip");

	/** The SplitEvaluator used to generate results */
	protected SplitEvaluator m_SplitEvaluator = new ClassifierSplitEvaluator();

	/** The names of any additional measures to look for in SplitEvaluators */
	protected String[] m_AdditionalMeasures = null;

	/** The name of the key field containing the dataset name */
	public static String DATASET_FIELD_NAME = "Dataset";

	/** The name of the key field containing the run number */
	public static String RUN_FIELD_NAME = "Run";

	/** The name of the key field containing the fold number */
	public static String FOLD_FIELD_NAME = "Fold";

	/** The name of the result field containing the timestamp */
	public static String TIMESTAMP_FIELD_NAME = "Date_time";

	/**
	 * Returns a string describing this result producer
	 * 
	 * @return a description of the result producer suitable for displaying in
	 *         the explorer/experimenter gui
	 */
	public String globalInfo() {
		return "Generates for each run, carries out an n-fold cross-validation, "
				+ "using the set SplitEvaluator to generate some results. If the class "
				+ "attribute is nominal, the dataset is stratified. Results for each fold "
				+ "are generated, so you may wish to use this in addition with an "
				+ "AveragingResultProducer to obtain averages for each run.";
	}

	/**
	 * Sets the dataset that results will be obtained for.
	 * 
	 * @param instances
	 *            a value of type 'Instances'.
	 */
	public void setInstances(Instances instances) {

		m_Instances = instances;
	}

	/**
	 * Sets the object to send results of each run to.
	 * 
	 * @param listener
	 *            a value of type 'ResultListener'
	 */
	public void setResultListener(ResultListener listener) {

		m_ResultListener = listener;
	}

	/**
	 * Set a list of method names for additional measures to look for in
	 * SplitEvaluators. This could contain many measures (of which only a subset
	 * may be produceable by the current SplitEvaluator) if an experiment is the
	 * type that iterates over a set of properties.
	 * 
	 * @param additionalMeasures
	 *            an array of measure names, null if none
	 */
	public void setAdditionalMeasures(String[] additionalMeasures) {
		m_AdditionalMeasures = additionalMeasures;

		if (m_SplitEvaluator != null) {
			System.err
					.println("CrossValidationResultProducer: setting additional "
							+ "measures for " + "split evaluator");
			m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
		}
	}

	/**
	 * Returns an enumeration of any additional measure names that might be in
	 * the SplitEvaluator
	 * 
	 * @return an enumeration of the measure names
	 */
	public Enumeration enumerateMeasures() {
		Vector newVector = new Vector();
		if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
			Enumeration en = ((AdditionalMeasureProducer) m_SplitEvaluator)
					.enumerateMeasures();
			while (en.hasMoreElements()) {
				String mname = (String) en.nextElement();
				newVector.addElement(mname);
			}
		}
		return newVector.elements();
	}

	/**
	 * Returns the value of the named measure
	 * 
	 * @param additionalMeasureName
	 *            the name of the measure to query for its value
	 * @return the value of the named measure
	 * @throws IllegalArgumentException
	 *             if the named measure is not supported
	 */
	public double getMeasure(String additionalMeasureName) {
		if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
			return ((AdditionalMeasureProducer) m_SplitEvaluator)
					.getMeasure(additionalMeasureName);
		} else {
			throw new IllegalArgumentException(
					"CrossValidationResultProducer: "
							+ "Can't return value for : "
							+ additionalMeasureName + ". "
							+ m_SplitEvaluator.getClass().getName() + " "
							+ "is not an AdditionalMeasureProducer");
		}
	}

	/**
	 * Gets a Double representing the current date and time. eg: 1:46pm on
	 * 20/5/1999 -> 19990520.1346
	 * 
	 * @return a value of type Double
	 */
	public static Double getTimestamp() {

		Calendar now = Calendar.getInstance(TimeZone.getTimeZone("UTC"));
		double timestamp = now.get(Calendar.YEAR) * 10000
				+ (now.get(Calendar.MONTH) + 1) * 100
				+ now.get(Calendar.DAY_OF_MONTH)
				+ now.get(Calendar.HOUR_OF_DAY) / 100.0
				+ now.get(Calendar.MINUTE) / 10000.0;
		return new Double(timestamp);
	}

	/**
	 * Prepare to generate results.
	 * 
	 * @throws Exception
	 *             if an error occurs during preprocessing.
	 */
	public void preProcess() throws Exception {

		if (m_SplitEvaluator == null) {
			throw new Exception("No SplitEvalutor set");
		}
		if (m_ResultListener == null) {
			throw new Exception("No ResultListener set");
		}
		m_ResultListener.preProcess(this);
	}

	/**
	 * Perform any postprocessing. When this method is called, it indicates that
	 * no more requests to generate results for the current experiment will be
	 * sent.
	 * 
	 * @throws Exception
	 *             if an error occurs
	 */
	public void postProcess() throws Exception {

		m_ResultListener.postProcess(this);

		if (m_debugOutput) {
			if (m_ZipDest != null) {
				m_ZipDest.finished();
				m_ZipDest = null;
			}
		}
	}

	/**
	 * Gets the keys for a specified run number. Different run numbers
	 * correspond to different randomizations of the data. Keys produced should
	 * be sent to the current ResultListener
	 * 
	 * @param run
	 *            the run number to get keys for.
	 * @throws Exception
	 *             if a problem occurs while getting the keys
	 */
	public void doRunKeys(int run) throws Exception {
		if (m_Instances == null) {
			throw new Exception("No Instances set");
		}
		/*
		 * // Randomize on a copy of the original dataset Instances runInstances
		 * = new Instances(m_Instances); runInstances.randomize(new
		 * Random(run)); if (runInstances.classAttribute().isNominal()) {
		 * runInstances.stratify(m_NumFolds); }
		 */
		for (int fold = 0; fold < m_NumFolds; fold++) {
			// Add in some fields to the key like run and fold number, dataset
			// name
			Object[] seKey = m_SplitEvaluator.getKey();
			Object[] key = new Object[seKey.length + 3];
			key[0] = Utils.backQuoteChars(m_Instances.relationName());
			key[1] = "" + run;
			key[2] = "" + (fold + 1);
			System.arraycopy(seKey, 0, key, 3, seKey.length);
			if (m_ResultListener.isResultRequired(this, key)) {
				try {
					m_ResultListener.acceptResult(this, key, null);
				} catch (Exception ex) {
					// Save the train and test datasets for debugging purposes?
					throw ex;
				}
			}
		}
	}

	/**
	 * Gets the results for a specified run number. Different run numbers
	 * correspond to different randomizations of the data. Results produced
	 * should be sent to the current ResultListener
	 * 
	 * @param run
	 *            the run number to get results for.
	 * @throws Exception
	 *             if a problem occurs while getting the results
	 */
	public void doRun(int run) throws Exception {

		if (getRawOutput()) {
			if (m_ZipDest == null) {
				m_ZipDest = new OutputZipper(m_OutputFile);
			}
		}

		if (m_Instances == null) {
			throw new Exception("No Instances set");
		}
		// Randomize on a copy of the original dataset
		Instances runInstances = new Instances(m_Instances);
		Random random = new Random(run);
		runInstances.randomize(random);
		if (runInstances.classAttribute().isNominal()) {
			runInstances.stratify(m_NumFolds);
		}
		for (int fold = 0; fold < m_NumFolds; fold++) {
			// Add in some fields to the key like run and fold number, dataset
			// name
			Object[] seKey = m_SplitEvaluator.getKey();
			Object[] key = new Object[seKey.length + 3];
			key[0] = Utils.backQuoteChars(m_Instances.relationName());
			key[1] = "" + run;
			key[2] = "" + (fold + 1);
			System.arraycopy(seKey, 0, key, 3, seKey.length);
			if (m_ResultListener.isResultRequired(this, key)) {
				Instances train = runInstances
						.trainCV(m_NumFolds, fold, random);
				Instances test = runInstances.testCV(m_NumFolds, fold);
				try {
					Object[] seResults = m_SplitEvaluator
							.getResult(train, test);
					Object[] results = new Object[seResults.length + 1];
					results[0] = getTimestamp();
					System.arraycopy(seResults, 0, results, 1, seResults.length);
					if (m_debugOutput) {
						String resultName = (""
								+ run
								+ "."
								+ (fold + 1)
								+ "."
								+ Utils.backQuoteChars(runInstances
										.relationName()) + "." + m_SplitEvaluator
								.toString()).replace(' ', '_');
						resultName = Utils.removeSubstring(resultName,
								"weka.classifiers.");
						resultName = Utils.removeSubstring(resultName,
								"weka.filters.");
						resultName = Utils.removeSubstring(resultName,
								"weka.attributeSelection.");
						m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(),
								resultName);
					}
					m_ResultListener.acceptResult(this, key, results);
				} catch (Exception ex) {
					// Save the train and test datasets for debugging purposes?
					throw ex;
				}
			}
		}
	}

	/**
	 * Gets the names of each of the columns produced for a single run. This
	 * method should really be static.
	 * 
	 * @return an array containing the name of each column
	 */
	public String[] getKeyNames() {

		String[] keyNames = m_SplitEvaluator.getKeyNames();
		// Add in the names of our extra key fields
		String[] newKeyNames = new String[keyNames.length + 3];
		newKeyNames[0] = DATASET_FIELD_NAME;
		newKeyNames[1] = RUN_FIELD_NAME;
		newKeyNames[2] = FOLD_FIELD_NAME;
		System.arraycopy(keyNames, 0, newKeyNames, 3, keyNames.length);
		return newKeyNames;
	}

	/**
	 * Gets the data types of each of the columns produced for a single run.
	 * This method should really be static.
	 * 
	 * @return an array containing objects of the type of each column. The
	 *         objects should be Strings, or Doubles.
	 */
	public Object[] getKeyTypes() {

		Object[] keyTypes = m_SplitEvaluator.getKeyTypes();
		// Add in the types of our extra fields
		Object[] newKeyTypes = new String[keyTypes.length + 3];
		newKeyTypes[0] = new String();
		newKeyTypes[1] = new String();
		newKeyTypes[2] = new String();
		System.arraycopy(keyTypes, 0, newKeyTypes, 3, keyTypes.length);
		return newKeyTypes;
	}

	/**
	 * Gets the names of each of the columns produced for a single run. This
	 * method should really be static.
	 * 
	 * @return an array containing the name of each column
	 */
	public String[] getResultNames() {

		String[] resultNames = m_SplitEvaluator.getResultNames();
		// Add in the names of our extra Result fields
		String[] newResultNames = new String[resultNames.length + 1];
		newResultNames[0] = TIMESTAMP_FIELD_NAME;
		System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length);
		return newResultNames;
	}

	/**
	 * Gets the data types of each of the columns produced for a single run.
	 * This method should really be static.
	 * 
	 * @return an array containing objects of the type of each column. The
	 *         objects should be Strings, or Doubles.
	 */
	public Object[] getResultTypes() {

		Object[] resultTypes = m_SplitEvaluator.getResultTypes();
		// Add in the types of our extra Result fields
		Object[] newResultTypes = new Object[resultTypes.length + 1];
		newResultTypes[0] = new Double(0);
		System.arraycopy(resultTypes, 0, newResultTypes, 1, resultTypes.length);
		return newResultTypes;
	}

	/**
	 * Gets a description of the internal settings of the result producer,
	 * sufficient for distinguishing a ResultProducer instance from another with
	 * different settings (ignoring those settings set through this interface).
	 * For example, a cross-validation ResultProducer may have a setting for the
	 * number of folds. For a given state, the results produced should be
	 * compatible. Typically if a ResultProducer is an OptionHandler, this
	 * string will represent the command line arguments required to set the
	 * ResultProducer to that state.
	 * 
	 * @return the description of the ResultProducer state, or null if no state
	 *         is defined
	 */
	public String getCompatibilityState() {

		String result = "-X " + m_NumFolds + " ";
		if (m_SplitEvaluator == null) {
			result += "<null SplitEvaluator>";
		} else {
			result += "-W " + m_SplitEvaluator.getClass().getName();
		}
		return result + " --";
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String outputFileTipText() {
		return "Set the destination for saving raw output. If the rawOutput "
				+ "option is selected, then output from the splitEvaluator for "
				+ "individual folds is saved. If the destination is a directory, "
				+ "then each output is saved to an individual gzip file; if the "
				+ "destination is a file, then each output is saved as an entry "
				+ "in a zip file.";
	}

	/**
	 * Get the value of OutputFile.
	 * 
	 * @return Value of OutputFile.
	 */
	public File getOutputFile() {

		return m_OutputFile;
	}

	/**
	 * Set the value of OutputFile.
	 * 
	 * @param newOutputFile
	 *            Value to assign to OutputFile.
	 */
	public void setOutputFile(File newOutputFile) {

		m_OutputFile = newOutputFile;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String numFoldsTipText() {
		return "Number of folds to use in cross validation.";
	}

	/**
	 * Get the value of NumFolds.
	 * 
	 * @return Value of NumFolds.
	 */
	public int getNumFolds() {

		return m_NumFolds;
	}

	/**
	 * Set the value of NumFolds.
	 * 
	 * @param newNumFolds
	 *            Value to assign to NumFolds.
	 */
	public void setNumFolds(int newNumFolds) {

		m_NumFolds = newNumFolds;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String rawOutputTipText() {
		return "Save raw output (useful for debugging). If set, then output is "
				+ "sent to the destination specified by outputFile";
	}

	/**
	 * Get if raw split evaluator output is to be saved
	 * 
	 * @return true if raw split evalutor output is to be saved
	 */
	public boolean getRawOutput() {
		return m_debugOutput;
	}

	/**
	 * Set to true if raw split evaluator output is to be saved
	 * 
	 * @param d
	 *            true if output is to be saved
	 */
	public void setRawOutput(boolean d) {
		m_debugOutput = d;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String splitEvaluatorTipText() {
		return "The evaluator to apply to the cross validation folds. "
				+ "This may be a classifier, regression scheme etc.";
	}

	/**
	 * Get the SplitEvaluator.
	 * 
	 * @return the SplitEvaluator.
	 */
	public SplitEvaluator getSplitEvaluator() {

		return m_SplitEvaluator;
	}

	/**
	 * Set the SplitEvaluator.
	 * 
	 * @param newSplitEvaluator
	 *            new SplitEvaluator to use.
	 */
	public void setSplitEvaluator(SplitEvaluator newSplitEvaluator) {

		m_SplitEvaluator = newSplitEvaluator;
		m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
	}

	/**
	 * Returns an enumeration describing the available options..
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {

		Vector newVector = new Vector(4);

		newVector.addElement(new Option(
				"\tThe number of folds to use for the cross-validation.\n"
						+ "\t(default 10)", "X", 1, "-X <number of folds>"));

		newVector.addElement(new Option("Save raw split evaluator output.",
				"D", 0, "-D"));

		newVector
				.addElement(new Option(
						"\tThe filename where raw output will be stored.\n"
								+ "\tIf a directory name is specified then then individual\n"
								+ "\toutputs will be gzipped, otherwise all output will be\n"
								+ "\tzipped to the named file. Use in conjuction with -D."
								+ "\t(default splitEvalutorOut.zip)", "O", 1,
						"-O <file/directory name/path>"));

		newVector.addElement(new Option(
				"\tThe full class name of a SplitEvaluator.\n"
						+ "\teg: weka.experiment.ClassifierSplitEvaluator",
				"W", 1, "-W <class name>"));

		if ((m_SplitEvaluator != null)
				&& (m_SplitEvaluator instanceof OptionHandler)) {
			newVector.addElement(new Option("", "", 0,
					"\nOptions specific to split evaluator "
							+ m_SplitEvaluator.getClass().getName() + ":"));
			Enumeration enu = ((OptionHandler) m_SplitEvaluator).listOptions();
			while (enu.hasMoreElements()) {
				newVector.addElement(enu.nextElement());
			}
		}
		return newVector.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -X &lt;number of folds&gt;
	 *  The number of folds to use for the cross-validation.
	 *  (default 10)
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 * Save raw split evaluator output.
	 * </pre>
	 * 
	 * <pre>
	 * -O &lt;file/directory name/path&gt;
	 *  The filename where raw output will be stored.
	 *  If a directory name is specified then then individual
	 *  outputs will be gzipped, otherwise all output will be
	 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of a SplitEvaluator.
	 *  eg: weka.experiment.ClassifierSplitEvaluator
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of the classifier.
	 *  eg: weka.classifiers.bayes.NaiveBayes
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;index&gt;
	 *  The index of the class for which IR statistics
	 *  are to be output. (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -I &lt;index&gt;
	 *  The index of an attribute to output in the
	 *  results. This attribute should identify an
	 *  instance in order to know which instances are
	 *  in the test set of a cross validation. if 0
	 *  no output (default 0).
	 * </pre>
	 * 
	 * <pre>
	 * -P
	 *  Add target and prediction columns to the result
	 *  for each fold.
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to classifier weka.classifiers.rules.ZeroR:
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * All options after -- will be passed to the split evaluator.
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {

		setRawOutput(Utils.getFlag('D', options));

		String fName = Utils.getOption('O', options);
		if (fName.length() != 0) {
			setOutputFile(new File(fName));
		}

		String numFolds = Utils.getOption('X', options);
		if (numFolds.length() != 0) {
			setNumFolds(Integer.parseInt(numFolds));
		} else {
			setNumFolds(10);
		}

		String seName = Utils.getOption('W', options);
		if (seName.length() == 0) {
			throw new Exception("A SplitEvaluator must be specified with"
					+ " the -W option.");
		}
		// Do it first without options, so if an exception is thrown during
		// the option setting, listOptions will contain options for the actual
		// SE.
		setSplitEvaluator((SplitEvaluator) Utils.forName(SplitEvaluator.class,
				seName, null));
		if (getSplitEvaluator() instanceof OptionHandler) {
			((OptionHandler) getSplitEvaluator()).setOptions(Utils
					.partitionOptions(options));
		}
	}

	/**
	 * Gets the current settings of the result producer.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {

		String[] seOptions = new String[0];
		if ((m_SplitEvaluator != null)
				&& (m_SplitEvaluator instanceof OptionHandler)) {
			seOptions = ((OptionHandler) m_SplitEvaluator).getOptions();
		}

		String[] options = new String[seOptions.length + 8];
		int current = 0;

		options[current++] = "-X";
		options[current++] = "" + getNumFolds();

		if (getRawOutput()) {
			options[current++] = "-D";
		}

		options[current++] = "-O";
		options[current++] = getOutputFile().getName();

		if (getSplitEvaluator() != null) {
			options[current++] = "-W";
			options[current++] = getSplitEvaluator().getClass().getName();
		}
		options[current++] = "--";

		System.arraycopy(seOptions, 0, options, current, seOptions.length);
		current += seOptions.length;
		while (current < options.length) {
			options[current++] = "";
		}
		return options;
	}

	/**
	 * Gets a text descrption of the result producer.
	 * 
	 * @return a text description of the result producer.
	 */
	public String toString() {

		String result = "CrossValidationResultProducer: ";
		result += getCompatibilityState();
		if (m_Instances == null) {
			result += ": <null Instances>";
		} else {
			result += ": " + Utils.backQuoteChars(m_Instances.relationName());
		}
		return result;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 1.17 $");
	}

	/**
	 * Quick test of timestamp
	 * 
	 * @param args
	 *            the commandline options
	 */
	public static void main(String[] args) {

		System.err.println(Utils
				.doubleToString(getTimestamp().doubleValue(), 4));
	}
} // CrossValidationResultProducer
